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Gene Expression and Coexpression Alterations Marking Evolution of Bladder Cancer

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Gene Expression and Coexpression Alterations Marking Evolution of Bladder Cancer medRxiv preprint doi: https://doi.org/10.1101/2021.06.15.21258890; this version posted June 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . 1 Rafael Stroggilos1,3, Maria Frantzi2, Jerome Zoidakis1, Emmanouil Mavrogeorgis1÷, Marika 2 Mokou2, Maria G Roubelakis3,4, Harald Mischak2, Antonia Vlahou1* 3 1. Systems Biology Center, Biomedical Research Foundation of the Academy of Athens, Athens, Greece. 4 2. Mosaiques Diagnostics GmbH, Hannover, Germany. 5 3. Laboratory of Biology, National and Kapodistrian University of Athens, School of Medicine, Athens, 6 Greece 7 4. Cell and Gene Therapy Laboratory, Biomedical Research Foundation of the Academy of Athens, Athens, 8 Greece. 9 ÷current address: Mosaiques Diagnostics GmbH, Hannover, Germany 10 * Corresponding author: 11 Antonia Vlahou, PhD 12 Biomedical Research Foundation, Academy of Athens 13 Soranou Efessiou 4, 14 11527, Athens, Greece 15 Tel: +30 210 6597506 16 Fax: +30 210 6597545 17 E-mail: [email protected] 18 19 TITLE 20 Gene expression and coexpression alterations marking evolution of bladder cancer 21 22 ABSTRACT 23 Despite advancements in therapeutics, Bladder Cancer (BLCA) constitutes a major clinical 24 burden, with locally advanced and metastatic cases facing poor survival rates. Aiming at 25 expanding our knowledge of BLCA molecular pathophysiology, we integrated 1,508 publicly 26 available, primary, well-characterized BLCA transcriptomes and investigated alterations in 27 gene expression with stage (T0-Ta-T1-T2-T3-T4). We identified 157 genes and several 28 pathways related prominently with cell cycle, showing a monotonically up- or down- 29 regulated trend with higher disease stage. Genome wide coexpression across stages further 30 revealed intrinsic and microenvironmental gene rewiring programs that shape BLCA 31 evolution. Novel associations between epigenetic factors (CBX7, ZFP2) and BLCA survival 32 were validated in external data. T0 together with advanced stages were heavily infiltrated 33 with immune cells, but of distinct populations. We found AIF1 to be a novel driver of 34 macrophage-based immunosuppression in T4 tumors. Our results suggest a continuum of 35 alterations with increasing malignancy. 36 37 INTRODUCTION NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2021.06.15.21258890; this version posted June 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . 38 Bladder Cancer (BLCA) accounts for approximately 200,000 annual deaths worldwide and is 39 considered the most expensive cancer to manage[1]. Advances in imaging technologies and 40 drug discovery have improved patient survival and quality of life [1]. However, early-stage 41 incidents [classified as non-muscle invasive (NMI)], still suffer from high rates of disease 42 recurrence, whereas advanced stage [classified as muscle invasive (MI)], and metastatic 43 cancers face poor outcomes [2]. 44 Advances in state-of-art molecular profiling technologies have enabled deeper investigations 45 of BLCA, expanding our current understanding of its molecular pathology. According to the 46 dual track model of bladder carcinogenesis [3], papillary-NMI and MI disease develop from 47 different sets of molecular alterations. Studies performing mutational profiling suggest that 48 low grade Ta tumors arise from activating mutations in either FGFR3 or HRAS, which 49 typically result in over-activation of the downstream Akt/PIK3CA/mTOR and RTK/MAPK 50 growth pathways [3]. In contrast, MIBC tumors are thought to develop from dysplastic Tis 51 lesions with non-functional (mutated) TP53 or RB1 tumor suppressive pathways [3]. 52 However, it remains unclear how these mutational signatures translate to different gene 53 expression routes. Moreover, the dual track model cannot explain adequately molecular 54 events driving transformation of a papillary-NMI tumor to MI, nor in the case of MIBC, 55 alterations happening before the dissemination to detrusor muscle. 56 In an effort to better understand molecular pathogenesis, various BLCA molecular subtypes 57 have been described [4-15]. Data supporting a continuum of alterations that likely drive 58 bladder carcinogenesis came from MI patients having tumors with a mosaic of both 59 intraepithelial (Tis) and papillary growth patterns, and from MI cancers having traits of 60 papillary-NMI related mutations. Approximately 22% of MI tumors present with activating 61 mutations in PI3KCA and homozygous deletion of the CDKN2A locus, respectively [9], while 62 CDKN2A deletion in MIBC has been observed to occur more frequently in FGFR3 mutated 63 than wild type tumors [16]. Interestingly, comparative mutational analysis between low 64 grade NMI, high grade NMI and MIBC revealed smooth increments or declines in the 65 frequency of mutations in driver genes (FGFR3, KDM6A, TP53, CDKN2A) with increasing 66 malignancy [17]. Additionally, multi-omics analysis of NMIBC identified dysfunctional TP53 67 and RB1 pathways in about ~25% of both Ta and T1 tumors [15]. 68 The clinical distinction between NMI and MI diseases and the current understanding of their 69 molecular determinants cannot describe adequately the events driving tumor evolution. On 70 the other hand, molecular subtypes may have important utilities in the diagnosis, prognosis, 71 and decision making, but unfortunately, they represent static entities and their dynamics 72 can only be studied between baseline diagnosis and future recurrences/progressions. In 73 contrast, stage as an ordinal variable reflecting tumor size and depth of invasion, offers a 74 better opportunity for studying the progressive alterations marking tumor initiation, growth, 75 dissemination to detrusor muscle and metastasis. 76 Gene expression studies comparing the stage profiles of BLCA typically involve small sample 77 sizes and are often limited to comparing NMI and MI. To obtain insight into the trajectories 78 of cancer evolution in BLCA, we collected publicly available transcriptomes from bulk tissue 79 samples, and performed a comprehensive stage analysis of 1,508 subjects with primary 80 BLCA, including a novel integrated pathway-to-network analysis. As the disease progresses 81 gradually to higher stages, our results indicate tumor dependencies on concerted alterations 82 of gene expression, with most prominent those involved in cell cycle regulation. medRxiv preprint doi: https://doi.org/10.1101/2021.06.15.21258890; this version posted June 21, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . 83 84 METHODS 85 Dataset mining 86 A comprehensive data mining strategy was employed to retrieve studies applying -omics 87 technologies in BLCA. The overall workflow is summarized in Figure 1. All genomic urothelial 88 cancer data from cBioportal (including The Cancer Genome Atlas) were downloaded 89 (5/1/2020). Gene Expression Omnibus (GEO) was queried for transcriptomics, additional 90 genomics or protein array datasets using the search terms “bladder cancer” and “urothelial 91 carcinoma”. We also queried ArrayExpress using the special filter “Array express data only” 92 to obtain any additional datasets missing from GEO. All cohort data published or updated 93 between 2010 and 2019, annotated as Homo sapiens, coming from tissue samples with 94 sample size >10, were initially retrieved (25/1/2020). All used datasets were published and 95 downloaded anonymized. 96 Integration of the transcriptomes 97 Microarray data were summarized to gene level with the package oligo [18]. Affymetrix 98 were normalized with the RMA method, while Illumina were filtered based on detection p 99 value < 0.05, followed by quantile normalization, addition of 1, and transformation to the 100 natural logarithmic scale. Data were annotated using biomaRt (v2.42.1) [19] and mircroarray 101 probes matching to multiple genes were excluded from downstream analysis. The probe 102 with the highest mean across arrays was selected as representative in cases where multiple 103 probes matched to the same gene. Merging of expression matrices was based on the Hugo 104 Gene Symbol using the intersection of genes between studies. 105 Adjustment for batch effects 106 To correct for batch effects across different studies in the discovery data, ComBat [20], 107 removeBatchEffect [21], and naiveRandRuv [22] were evaluated. ComBat performed best 108 and was chosen for further use (manuscript in preparation). The quality of corrected data 109 was assessed with BatchQC [23] (Supplementary Figures 1-2), with boxplots of expression 110 distribution per sample and principal component analysis plot of sample relationships ,with 111 gene expression comparisons of housekeeping to other genes, and with a set of 12 BLCA 112 markers with known regulation across normal-NMI-MI or across normal-low grade- high 113 grade disease (Supplementary Figure 3). 114 Differential expression,
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